A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments

Omer Levy, Anders Søgaard, Yoav Goldberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

While cross-lingual word embeddings have been studied extensively in recent years, the qualitative differences between the different algorithms remain vague. We observe that whether or not an algorithm uses a particular feature set (sentence IDs) accounts for a significant performance gap among these algorithms. This feature set is also used by traditional alignment algorithms, such as IBM Model-1, which demonstrate similar performance to state-of-the-art embedding algorithms on a variety of benchmarks. Overall, we observe that different algorithmic approaches for utilizing the sentence ID feature space result in similar performance. This paper draws both empirical and theoretical parallels between the embedding and alignment literature, and suggests that adding additional sources of information, which go beyond the traditional signal of bilingual sentence-aligned corpora, may substantially improve cross-lingual word embeddings, and that future baselines should at least take such features into account.
Original languageEnglish
Title of host publicationProceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
EditorsMirella Lapata, Phil Blunsom, Alexander Koller
PublisherAssociation for Computational Linguistics (ACL)
Pages765-774
Number of pages10
ISBN (Electronic)9781510838604
DOIs
StatePublished - 1 Apr 2017
Externally publishedYes
Event15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain
Duration: 3 Apr 20177 Apr 2017

Conference

Conference15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
Country/TerritorySpain
CityValencia
Period3/04/177/04/17

Fingerprint

Dive into the research topics of 'A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments'. Together they form a unique fingerprint.

Cite this